Langflow is an open-source, low-code platform for visually designing, building, and deploying AI-powered agents and RAG workflows. With approximately 146K GitHub stars, it has become the fastest-growing visual AI builder, going from zero to 146K stars in just three years.
Langflow provides a drag-and-drop visual interface built on top of the LangChain framework, enabling rapid prototyping of complex AI pipelines without extensive coding. It bridges the gap between LangChain's powerful Python API and accessibility for non-engineers, reducing AI application development time from weeks to hours. Langflow supports RAG workflows, multi-agent orchestration, and deployment as REST APIs, making it suitable for both prototyping and production use cases.
Key Features
Visual Drag-and-Drop Builder — Node-based canvas where LangChain primitives (prompts, retrievers, agents) are connected via data flows
RAG Workflows — Native support for document ingestion, vector store retrieval, and grounded generation
Multi-Agent Orchestration — Conversational agents with memory management, tool use, and stateful interactions
MCP Support — Model Context Protocol integration for agentic standards
REST API Deployment — One-click export of flows as production API endpoints
LangGraph Integration — Stateful, cyclical workflows with conditional branching
Component Ecosystem — Bundles for WatsonxAI, Google GenAI, Composio, JigsawStack, Cleanlab AI
PWA Support — Progressive web app functionality via web app manifests
Architecture
Langflow's architecture layers a React-based visual frontend on top of a Python/LangChain backend:
graph TD
subgraph Frontend["React Visual Frontend"]
A[Canvas Editor]
B[Component Library]
C[Flow Runner Controls]
end
subgraph Backend["Python Backend: FastAPI"]
D[LangChain Engine]
E[LangGraph Runtime]
F[REST API Exporter]
end
Frontend --> Backend
D --> G[LLMs: OpenAI / Gemini]
E --> H[Vector DBs: Pinecone / Chroma]
F --> I[Tools: Search / Custom]
Code Example
Using Langflow's Python API to run a flow programmatically:
import requests
LANGFLOW_URL ="http://localhost:7860"
FLOW_ID ="your-flow-id"def run_langflow_agent(query: str) ->str:
"""Execute a Langflow flow via its REST API."""
endpoint = f"{LANGFLOW_URL}/api/v1/run/{FLOW_ID}"
payload ={"input_value": query,"output_type": "chat","input_type": "chat","tweaks": {}}
response = requests.post(endpoint, json=payload)
response.raise_for_status()
data = response.json()return data["outputs"][0]["outputs"][0]["results"]["message"]["text"]
result = run_langflow_agent("What are the benefits of RAG?")print(result)
Relationship to LangChain
Langflow is explicitly built on top of LangChain, serving as its visual companion:
Exposes LangChain components (agents, chains, memory, RAG, tools) through drag-and-drop
Depends on LangChain/LangSmith libraries and LangGraph for advanced workflows
Enables non-engineers to prototype production-ready flows
LangChain handles underlying execution while Langflow provides the visual abstraction
Recent Updates (2025-2026)
Version 1.4.x with frequent iterations on UI/UX and performance